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Archive of posts filed under the Stan category.

Seeking postdoc (or contractor) for next generation Stan language research and development

The Stan group at Columbia is looking to hire a postdoc* to work on the next generation compiler for the Stan open-source probabilistic programming language. Ideally, a candidate will bring language development experience and also have research interests in a related field such as programming languages, applied statistics, numerical analysis, or statistical computation. The language […]

Yes, you can include prior information on quantities of interest, not just on parameters in your model

Nick Kavanagh writes: I studied economics in college and never heard more than a passing reference to Bayesian stats. I started to encounter Bayesian concepts in the workplace and decided to teach myself on the side. I was hoping to get your advice on a problem that I recently encountered. It has to do with […]

Stancon is happening now.

Hi, everyone!

Why does my academic lab keep growing?

Andrew, Breck, and I are struggling with the Stan group funding at Columbia just like most small groups in academia. The short story is that to apply for enough grants to give us a decent chance of making payroll in the following year, we have to apply for so many that our expected amount of […]

The Economist does Mister P

Elliott Morris points us to this magazine article, “If everyone had voted, Hillary Clinton would probably be president,” which reports: Close observers of America know that the rules of its democracy often favour Republicans. But the party’s biggest advantage may be one that is rarely discussed: turnout is just 60%, low for a rich country. […]

Software release strategies

Scheduled release strategy Stan’s moved to a scheduled release strategy where we’ll simply release whatever we have every three months. The Stan 2.20 release just went out last week. So you can expect Stan 2.21 in three months. Our core releases include the math library, the language compiler, and CmdStan. That requires us to keep […]

Healthier kids: Using Stan to get more information out of pediatric respiratory data

Robert Mahar, John Carlin, Sarath Ranganathan, Anne-Louise Ponsonby, Peter Vuillermin, and Damjan Vukcevic write: Paediatric respiratory researchers have widely adopted the multiple-breath washout (MBW) test because it allows assessment of lung function in unsedated infants and is well suited to longitudinal studies of lung development and disease. However, a substantial proportion of MBW tests in […]

They’re looking to hire someone with good working knowledge of Bayesian inference algorithms development for multilevel statistical models and mathematical modeling of physiological systems.

Frederic Bois writes: We have an immediate opening for a highly motivated research / senior scientist with good working knowledge of Bayesian inference algorithms development for multilevel statistical models and mathematical modelling of physiological systems. The successful candidate will assist with the development of deterministic or stochastic methods and algorithms applicable to systems pharmacology/biology models […]

Read this: it’s about importance sampling!

Importance sampling plays an odd role in statistical computing. It’s an old-fashioned idea and can behave just horribly if applied straight-up—but it keeps arising in different statistics problems. Aki came up with Pareto-smoothed importance sampling (PSIS) for leave-one-out cross-validation. We recently revised the PSIS article and Dan Simpson wrote a useful blog post about it […]

All I need is time, a moment that is mine, while I’m in between

You’re an ordinary boy and that’s the way I like it – Magic Dirt Look. I’ll say something now, so it’s off my chest. I hate order statisics. I loathe them. I detest them. I wish them nothing but ill and strife. They are just awful. And I’ve spent the last god only knows how long […]

How does Stan work? A reading list.

Bob writes, to someone who is doing work on the Stan language: The basic execution structure of Stan is in the JSS paper (by Bob Carpenter, Andrew Matt Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell) and in the reference manual. The details of autodiff are in […]

AnnoNLP conference on data coding for natural language processing

This workshop should be really interesting: Aggregating and analysing crowdsourced annotations for NLP EMNLP Workshop. November 3–4, 2019. Hong Kong. Silviu Paun and Dirk Hovy are co-organizing it. They’re very organized and know this area as well as anyone. I’m on the program committee, but won’t be able to attend. I really like the problem […]

Question 3 of our Applied Regression final exam (and solution to question 2)

Here’s question 3 of our exam: Here is a fitted model from the Bangladesh analysis predicting whether a person with high-arsenic drinking water will switch wells, given the arsenic level in their existing well and the distance to the nearest safe well. glm(formula = switch ~ dist100 + arsenic, family=binomial(link=”logit”)) coef.est (Intercept) 0.00 0.08 […]

New! from Bales/Pourzanjani/Vehtari/Petzold: Selecting the Metric in Hamiltonian Monte Carlo

Ben Bales, Arya Pourzanjani, Aki Vehtari, and Linda Petzold write: We present a selection criterion for the Euclidean metric adapted during warmup in a Hamiltonian Monte Carlo sampler that makes it possible for a sampler to automatically pick the metric based on the model and the availability of warmup draws. Additionally, we present a new […]

Peter Ellis on Forecasting Antipodal Elections with Stan

I liked this intro to Peter Ellis from Rob J. Hyndman’s talk announcement: He [Peter Ellis] started forecasting elections in New Zealand as a way to learn how to use Stan, and the hobby has stuck with him since he moved back to Australia in late 2018. You may remember Peter from my previous post […]

Maintenance cost is quadratic in the number of features

Bob Carpenter shares this story illustrating the challenges of software maintenance. Here’s Bob: This started with the maintenance of upgrading to the new Boost version 1.69, which is this pull request: for this issue: The issue happens first, then the pull request, then the fun of debugging starts. Today’s story starts an issue […]

Stan examples in Harezlak, Ruppert and Wand (2018) Semiparametric Regression with R

I saw earlier drafts of this when it was in preparation and they were great. Jarek Harezlak, David Ruppert and Matt P. Wand. 2018. Semiparametric Regression with R. UseR! Series. Springer. I particularly like the careful evaluation of variational approaches. I also very much like that it’s packed with visualizations and largely based on worked […]

We shouldn’t’ve called it “Stan”; I should’ve listened to Bob and Hadley

Hadley told me that one reason he came up with the name ggplot was that it would be uniquely findable on Google. When we were writing Stan and I suggested naming it Stan, Bob pointed out the googling argument but I just loved the name Stan, I loved the Ulam connection and having this friendly […]

Several post-doc positions in probabilistic programming etc. in Finland

There are several open post-doc positions in Aalto and University of Helsinki in 1. probabilistic programming, 2. simulator-based inference, 3. data-efficient deep learning, 4. privacy preserving and secure methods, 5. interactive AI. All these research programs are connected and collaborating. I (Aki) am the coordinator for the project 1 and contributor in the others. Overall […]

Postdoctoral position in Vancouver! Using Stan! Working on wine! For reals.

Lizzie Wolkovich writes that she is hiring someone to help build Stan models for winegrapes. Here’s the ad: Postdoctoral Fellow in Winegrape Research—University of British Columbia The Temporal Ecology Lab is looking for a bright, motivated and collaborative researcher to join the lab and develop new winegrape models using Stan ( The project combines decades […]